Guía de aprendizaje automático
1.0.0
Nota: Puede convertir fácilmente este archivo Markdown a un PDF en VSCode utilizando esta práctica extensión Markdown PDF.
Marcos de aprendizaje automático/aprendizaje profundo.
Recursos de aprendizaje para ML
Marcos, bibliotecas y herramientas de aprendizaje automático
Algoritmos
Desarrollo de PyTorch
Desarrollo de TensorFlow
Desarrollo central de aprendizaje automático
Desarrollo de aprendizaje profundo
Desarrollo del aprendizaje por refuerzo
Desarrollo de visión por computadora
Desarrollo del procesamiento del lenguaje natural (PNL)
Bioinformática
Desarrollo CUDA
DesarrolloMATLAB
Desarrollo C/C++
Desarrollo Java
Desarrollo de Python
Desarrollo escalal
Desarrollo R
Desarrollo Julia
Volver a la cima
Machine Learning es una rama de la inteligencia artificial (IA) centrada en crear aplicaciones utilizando algoritmos que aprenden de modelos de datos y mejoran su precisión con el tiempo sin necesidad de programación.
Volver a la cima
Mejores prácticas de procesamiento del lenguaje natural (NLP) de Microsoft
El libro de recetas de conducción autónoma de Microsoft
Aprendizaje automático de Azure: aprendizaje automático como servicio | MicrosoftAzure
Cómo ejecutar Jupyter Notebooks en su espacio de trabajo de Azure Machine Learning
Aprendizaje automático e inteligencia artificial | Servicios web de Amazon
Programación de cuadernos de Jupyter en instancias efímeras de Amazon SageMaker
IA y aprendizaje automático | Nube de Google
Uso de Jupyter Notebooks con Apache Spark en Google Cloud
Aprendizaje automático | Desarrollador de Apple
Inteligencia artificial y piloto automático | tesla
Herramientas de metainteligencia artificial | Facebook
Tutoriales de PyTorch
Tutoriales de TensorFlow
JupyterLab
Difusión estable con Core ML en Apple Silicon
Volver a la cima
Aprendizaje automático de la Universidad de Stanford de Andrew Ng | Coursera
Cursos de capacitación y certificación de AWS para aprendizaje automático (ML)
Programa de becas de aprendizaje automático para Microsoft Azure | Udacidad
Certificado de Microsoft: asociado científico de datos de Azure
Certificado de Microsoft: ingeniero asociado de inteligencia artificial de Azure
Capacitación e implementación de Azure Machine Learning
Aprendizaje Aprendizaje automático e inteligencia artificial de Google Cloud Training
Curso intensivo de aprendizaje automático para Google Cloud
Cursos de aprendizaje automático en línea | Udemy
Cursos de aprendizaje automático en línea | Coursera
Aprenda el aprendizaje automático con cursos y clases en línea | edX
Volver a la cima
Introducción al aprendizaje automático (PDF)
Inteligencia artificial: un enfoque moderno por Stuart J. Russel y Peter Norvig
Aprendizaje profundo por Ian Goodfellow, Yoshoua Bengio y Aaron Courville
El libro de cien páginas sobre aprendizaje automático de Andriy Burkov
Aprendizaje automático por Tom M. Mitchell
Programación de inteligencia colectiva: creación de aplicaciones web 2.0 inteligentes por Toby Segaran
Aprendizaje automático: una perspectiva algorítmica, segunda edición
Reconocimiento de patrones y aprendizaje automático por Christopher M. Bishop
Procesamiento del lenguaje natural con Python por Steven Bird, Ewan Klein y Edward Loper
Aprendizaje automático de Python: un enfoque técnico del aprendizaje automático para principiantes por Leonard Eddison
Razonamiento bayesiano y aprendizaje automático por David Barber
Aprendizaje automático para principiantes absolutos: una introducción sencilla en inglés por Oliver Theobald
Aprendizaje automático en acción por Ben Wilson
Aprendizaje automático práctico con Scikit-Learn, Keras y TensorFlow: conceptos, herramientas y técnicas para construir sistemas inteligentes por Aurélien Géron
Introducción al aprendizaje automático con Python: una guía para científicos de datos por Andreas C. Müller y Sarah Guido
Aprendizaje automático para piratas informáticos: estudios de casos y algoritmos para comenzar por Drew Conway y John Myles White
Los elementos del aprendizaje estadístico: minería de datos, inferencia y predicción por Trevor Hastie, Robert Tibshirani y Jerome Friedman
Patrones de aprendizaje automático distribuido: libro (lectura gratuita en línea) + código
Aprendizaje automático del mundo real [Capítulos gratuitos]
Introducción al aprendizaje estadístico - Libro + Código R
Elementos del aprendizaje estadístico - Libro
Think Bayes - Libro + Código Python
Minería de conjuntos de datos masivos
Un primer encuentro con el aprendizaje automático
Introducción al aprendizaje automático: Alex Smola y SVN Vishwanathan
Una teoría probabilística del reconocimiento de patrones
Introducción a la recuperación de información
Previsión: principios y práctica.
Introducción al aprendizaje automático - Amnon Shashua
Aprendizaje por refuerzo
Aprendizaje automático
Una búsqueda de la IA
Programación R para ciencia de datos
Minería de datos: herramientas y técnicas prácticas de aprendizaje automático
Aprendizaje automático con TensorFlow
Sistemas de aprendizaje automático
Fundamentos del aprendizaje automático: Mehryar Mohri, Afshin Rostamizadeh y Ameet Talwalkar
Búsqueda impulsada por IA: Trey Grainger, Doug Turnbull, Max Irwin -
Métodos conjuntos para el aprendizaje automático - Gautam Kunapuli
Ingeniería de aprendizaje automático en acción - Ben Wilson
Aprendizaje automático que preserva la privacidad: J. Morris Chang, Di Zhuang, G. Dumindu Samaraweera
Aprendizaje automático automatizado en acción: Qingquan Song, Haifeng Jin y Xia Hu
Patrones de aprendizaje automático distribuido - Yuan Tang
Gestión de proyectos de aprendizaje automático: desde el diseño hasta la implementación - Simon Thompson
Aprendizaje automático causal - Robert Ness
Optimización bayesiana en acción - Quan Nguyen
Algoritmos de aprendizaje automático en profundidad) - Vadim Smolyakov
Algoritmos de optimización - Alaa Khamis
Aumento práctico de gradiente por Guillaume Saupin
Volver a la cima
Volver a la cima
TensorFlow es una plataforma de código abierto de un extremo a otro para el aprendizaje automático. Tiene un ecosistema integral y flexible de herramientas, bibliotecas y recursos comunitarios que permite a los investigadores impulsar lo último en ML y a los desarrolladores crear e implementar fácilmente aplicaciones basadas en ML.
Keras es una API de redes neuronales de alto nivel, escrita en Python y capaz de ejecutarse sobre TensorFlow, CNTK o Theano. Fue desarrollada con el objetivo de permitir una experimentación rápida. Es capaz de ejecutarse sobre TensorFlow, Microsoft Cognitive Toolkit, R, Theano o PlaidML.
PyTorch es una biblioteca para el aprendizaje profundo de datos de entrada irregulares, como gráficos, nubes de puntos y variedades. Desarrollado principalmente por el laboratorio de investigación de inteligencia artificial de Facebook.
Amazon SageMaker es un servicio totalmente administrado que brinda a todos los desarrolladores y científicos de datos la capacidad de crear, entrenar e implementar modelos de aprendizaje automático (ML) rápidamente. SageMaker elimina el trabajo pesado de cada paso del proceso de aprendizaje automático para facilitar el desarrollo de modelos de alta calidad.
Azure Databricks es un servicio de análisis de big data rápido y colaborativo basado en Apache Spark diseñado para ciencia e ingeniería de datos. Azure Databricks configura su entorno Apache Spark en minutos, escala automáticamente y colabora en proyectos compartidos en un espacio de trabajo interactivo. Azure Databricks admite Python, Scala, R, Java y SQL, así como bibliotecas y marcos de ciencia de datos, incluidos TensorFlow, PyTorch y scikit-learn.
Microsoft Cognitive Toolkit (CNTK) es un conjunto de herramientas de código abierto para el aprendizaje profundo distribuido de nivel comercial. Describe las redes neuronales como una serie de pasos computacionales a través de un gráfico dirigido. CNTK permite al usuario realizar y combinar fácilmente tipos de modelos populares, como DNN de avance, redes neuronales convolucionales (CNN) y redes neuronales recurrentes (RNN/LSTM). CNTK implementa el aprendizaje de descenso de gradiente estocástico (SGD, retropropagación de errores) con diferenciación y paralelización automáticas en múltiples GPU y servidores.
Apple CoreML es un marco que ayuda a integrar modelos de aprendizaje automático en su aplicación. Core ML proporciona una representación unificada para todos los modelos. Su aplicación utiliza API Core ML y datos de usuario para hacer predicciones y entrenar o ajustar modelos, todo en el dispositivo del usuario. Un modelo es el resultado de aplicar un algoritmo de aprendizaje automático a un conjunto de datos de entrenamiento. Se utiliza un modelo para hacer predicciones basadas en nuevos datos de entrada.
Apache OpenNLP es una biblioteca de código abierto para un conjunto de herramientas basado en aprendizaje automático que se utiliza en el procesamiento de texto en lenguaje natural. Cuenta con una API para casos de uso como reconocimiento de entidades nombradas, detección de oraciones, etiquetado POS (parte del discurso), extracción de funciones de tokenización, fragmentación, análisis y resolución de correferencia.
Apache Airflow es una plataforma de gestión de flujo de trabajo de código abierto creada por la comunidad para crear, programar y monitorear flujos de trabajo mediante programación. Instalar. Principios. Escalable. Airflow tiene una arquitectura modular y utiliza una cola de mensajes para organizar una cantidad arbitraria de trabajadores. Airflow está listo para escalar hasta el infinito.
Open Neural Network Exchange (ONNX) es un ecosistema abierto que permite a los desarrolladores de IA elegir las herramientas adecuadas a medida que evoluciona su proyecto. ONNX proporciona un formato de código abierto para modelos de IA, tanto de aprendizaje profundo como de aprendizaje automático tradicional. Define un modelo de gráfico de cálculo extensible, así como definiciones de operadores integrados y tipos de datos estándar.
Apache MXNet es un marco de aprendizaje profundo diseñado para brindar eficiencia y flexibilidad. Le permite combinar programación simbólica e imperativa para maximizar la eficiencia y la productividad. En esencia, MXNet contiene un programador de dependencias dinámico que paraleliza automáticamente operaciones simbólicas e imperativas sobre la marcha. Una capa de optimización de gráficos además hace que la ejecución simbólica sea rápida y eficiente en la memoria. MXNet es portátil y liviano, y se escala de manera efectiva a múltiples GPU y múltiples máquinas. Soporte para Python, R, Julia, Scala, Go, Javascript y más.
AutoGluon es un conjunto de herramientas para aprendizaje profundo que automatiza las tareas de aprendizaje automático, lo que le permite lograr fácilmente un sólido rendimiento predictivo en sus aplicaciones. Con solo unas pocas líneas de código, puede entrenar e implementar modelos de aprendizaje profundo de alta precisión en datos tabulares, de imágenes y de texto.
Anaconda es una plataforma de ciencia de datos muy popular para aprendizaje automático y aprendizaje profundo que permite a los usuarios desarrollar modelos, entrenarlos e implementarlos.
PlaidML es un compilador de tensor avanzado y portátil para permitir el aprendizaje profundo en computadoras portátiles, dispositivos integrados u otros dispositivos donde el hardware informático disponible no es compatible o la pila de software disponible contiene restricciones de licencia desagradables.
OpenCV es una biblioteca altamente optimizada que se centra en aplicaciones de visión por computadora en tiempo real. Las interfaces C++, Python y Java son compatibles con Linux, MacOS, Windows, iOS y Android.
Scikit-Learn es un módulo de Python para aprendizaje automático creado sobre SciPy, NumPy y matplotlib, lo que facilita la aplicación de implementaciones sólidas y simples de muchos algoritmos populares de aprendizaje automático.
Weka es un software de aprendizaje automático de código abierto al que se puede acceder a través de una interfaz gráfica de usuario, aplicaciones de terminal estándar o una API de Java. Se utiliza ampliamente para la enseñanza, la investigación y aplicaciones industriales, contiene una gran cantidad de herramientas integradas para tareas estándar de aprendizaje automático y, además, brinda acceso transparente a cajas de herramientas conocidas como scikit-learn, R y Deeplearning4j.
Caffe es un marco de aprendizaje profundo creado teniendo en cuenta la expresión, la velocidad y la modularidad. Está desarrollado por Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) y contribuyentes de la comunidad.
Theano es una biblioteca de Python que le permite definir, optimizar y evaluar expresiones matemáticas que involucran matrices multidimensionales de manera eficiente, incluida una estrecha integración con NumPy.
nGraph es una biblioteca, compilador y tiempo de ejecución de C++ de código abierto para aprendizaje profundo. nGraph Compiler tiene como objetivo acelerar el desarrollo de cargas de trabajo de IA utilizando cualquier marco de aprendizaje profundo y su implementación en una variedad de objetivos de hardware. Proporciona libertad, rendimiento y facilidad de uso a los desarrolladores de IA.
NVIDIA cuDNN es una biblioteca de primitivas acelerada por GPU para redes neuronales profundas. cuDNN proporciona implementaciones altamente optimizadas para rutinas estándar como convolución hacia adelante y hacia atrás, agrupación, normalización y capas de activación. cuDNN acelera marcos de aprendizaje profundo ampliamente utilizados, incluidos Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch y TensorFlow.
Huginn es un sistema autohospedado para crear agentes que realizan tareas automatizadas en línea. Puede leer la web, observar eventos y tomar medidas en su nombre. Los agentes de Huginn crean y consumen eventos, propagándolos a lo largo de un gráfico dirigido. Piense en ello como una versión pirateable de IFTTT o Zapier en su propio servidor.
Netron es un visor de modelos de redes neuronales, aprendizaje profundo y aprendizaje automático. Es compatible con ONNX, TensorFlow Lite, Caffe, Keras, Darknet, PaddlePaddle, ncnn, MNN, Core ML, RKNN, MXNet, MindSpore Lite, TNN, Barracuda, Tengine, CNTK, TensorFlow.js, Caffe2 y UFF.
La dopamina es un marco de investigación para la creación rápida de prototipos de algoritmos de aprendizaje por refuerzo.
DALI es una biblioteca acelerada por GPU que contiene bloques de construcción altamente optimizados y un motor de ejecución para el procesamiento de datos para acelerar las aplicaciones de inferencia y entrenamiento de aprendizaje profundo.
MindSpore Lite es un nuevo marco de inferencia/entrenamiento de aprendizaje profundo de código abierto que podría usarse para escenarios móviles, de borde y de nube.
Darknet es un marco de red neuronal de código abierto escrito en C y CUDA. Es rápido, fácil de instalar y admite cálculo de CPU y GPU.
PaddlePaddle es una plataforma de aprendizaje profundo fácil de usar, eficiente, flexible y escalable, desarrollada originalmente por científicos e ingenieros de Baidu con el propósito de aplicar el aprendizaje profundo a muchos productos de Baidu.
GoogleNotebookLM es una herramienta experimental de inteligencia artificial que utiliza el poder de los modelos de lenguaje combinados con su contenido existente para obtener información crítica más rápidamente. Similar a un asistente de investigación virtual que puede resumir hechos, explicar ideas complejas y generar nuevas conexiones basadas en las fuentes que seleccione.
Unilm es una capacitación previa autosupervisada a gran escala que abarca tareas, idiomas y modalidades.
Semantic Kernel (SK) es un SDK liviano que permite la integración de modelos de lenguajes grandes (LLM) de IA con lenguajes de programación convencionales. El modelo de programación extensible SK combina funciones semánticas de lenguaje natural, funciones nativas de código tradicional y memoria basada en incrustaciones, lo que desbloquea un nuevo potencial y agrega valor a las aplicaciones con IA.
Pandas AI es una biblioteca de Python que integra capacidades de inteligencia artificial generativa en Pandas, haciendo que los marcos de datos sean conversacionales.
NCNN es un marco de inferencia de redes neuronales de alto rendimiento optimizado para la plataforma móvil.
MNN es un marco de aprendizaje profundo increíblemente rápido y liviano, probado en casos de uso críticos para el negocio en Alibaba.
MediaPipe está optimizado para el rendimiento de un extremo a otro en una amplia gama de plataformas. Ver demostraciones Más información ML complejo en el dispositivo, simplificado Hemos abstraído las complejidades de hacer que el ML en el dispositivo sea personalizable, esté listo para producción y sea accesible en todas las plataformas.
MegEngine es un marco de aprendizaje profundo rápido, escalable y fácil de usar con 3 características clave: Marco unificado tanto para entrenamiento como para inferencia.
ML.NET es una biblioteca de aprendizaje automático diseñada como una plataforma extensible para que pueda consumir otros marcos de aprendizaje automático populares (TensorFlow, ONNX, Infer.NET y más) y tener acceso a aún más escenarios de aprendizaje automático, como clasificación de imágenes, detección de objetos y más.
Ludwig es un marco de aprendizaje automático declarativo que facilita la definición de canales de aprendizaje automático mediante un sistema de configuración basado en datos simple y flexible.
MMdnn es una herramienta integral y transversal para convertir, visualizar y diagnosticar modelos de aprendizaje profundo (DL). "MM" significa gestión de modelos y "dnn" es el acrónimo de red neuronal profunda. Convierta modelos entre Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx y CoreML.
Horovod es un marco de capacitación distribuido de aprendizaje profundo para TensorFlow, Keras, PyTorch y Apache MXNet.
Vaex es una biblioteca de Python de alto rendimiento para marcos de datos fuera del núcleo (similares a Pandas), para visualizar y explorar grandes conjuntos de datos tabulares.
GluonTS es un paquete de Python para modelado probabilístico de series de tiempo, centrándose en modelos basados en aprendizaje profundo, basados en PyTorch y MXNet.
MindsDB es un servidor ML-SQL que permite flujos de trabajo de aprendizaje automático para las bases de datos y almacenes de datos más potentes que utilizan SQL.
Jupyter Notebook es una aplicación web de código abierto que le permite crear y compartir documentos que contienen código en vivo, ecuaciones, visualizaciones y texto narrativo. Jupyter se utiliza ampliamente en industrias que realizan limpieza y transformación de datos, simulación numérica, modelado estadístico, visualización de datos, ciencia de datos y aprendizaje automático.
Apache Spark es un motor de análisis unificado para el procesamiento de datos a gran escala. Proporciona API de alto nivel en Scala, Java, Python y R, y un motor optimizado que admite gráficos de cálculo general para el análisis de datos. También admite un amplio conjunto de herramientas de nivel superior que incluyen Spark SQL para SQL y DataFrames, MLlib para aprendizaje automático, GraphX para procesamiento de gráficos y Structured Streaming para procesamiento de secuencias.
Apache Spark Connector para SQL Server y Azure SQL es un conector de alto rendimiento que le permite utilizar datos transaccionales en análisis de big data y conservar resultados para consultas o informes ad hoc. El conector le permite utilizar cualquier base de datos SQL, local o en la nube, como fuente de datos de entrada o receptor de datos de salida para trabajos de Spark.
Apache PredictionIO es un marco de aprendizaje automático de código abierto para desarrolladores, científicos de datos y usuarios finales. Admite la recopilación de eventos, la implementación de algoritmos, la evaluación y la consulta de resultados predictivos a través de API REST. Se basa en servicios escalables de código abierto como Hadoop, HBase (y otras bases de datos), Elasticsearch, Spark e implementa lo que se llama arquitectura Lambda.
Cluster Manager para Apache Kafka (CMAK) es una herramienta para administrar clústeres de Apache Kafka.
BigDL es una biblioteca distribuida de aprendizaje profundo para Apache Spark. Con BigDL, los usuarios pueden escribir sus aplicaciones de aprendizaje profundo como programas Spark estándar, que pueden ejecutarse directamente sobre los clústeres Spark o Hadoop existentes.
Eclipse Deeplearning4J (DL4J) es un conjunto de proyectos destinados a respaldar todas las necesidades de una aplicación de aprendizaje profundo basada en JVM (Scala, Kotlin, Clojure y Groovy). Esto significa comenzar con los datos sin procesar, cargarlos y preprocesarlos desde cualquier lugar y en cualquier formato para construir y ajustar una amplia variedad de redes de aprendizaje profundo simples y complejas.
Tensorman es una utilidad para una fácil gestión de contenedores de Tensorflow desarrollada por System76. Tensorman permite que Tensorflow opere en un entorno aislado del resto del sistema. Este entorno virtual puede funcionar independientemente del sistema base, lo que le permite utilizar cualquier versión de Tensorflow en cualquier versión de una distribución de Linux que admita el tiempo de ejecución de Docker.
Numba es un compilador de optimización compatible con NumPy de código abierto para Python patrocinado por Anaconda, Inc. Utiliza el proyecto del compilador LLVM para generar código de máquina a partir de la sintaxis de Python. Numba puede compilar un gran subconjunto de Python centrado numéricamente, incluidas muchas funciones de NumPy. Además, Numba admite la paralelización automática de bucles, la generación de código acelerado por GPU y la creación de ufuncs y devoluciones de llamadas en C.
Chainer es un marco de aprendizaje profundo basado en Python que busca flexibilidad. Proporciona API de diferenciación automática basadas en el enfoque de definición por ejecución (gráficos computacionales dinámicos), así como API de alto nivel orientadas a objetos para construir y entrenar redes neuronales. También admite CUDA/cuDNN utilizando CuPy para entrenamiento e inferencia de alto rendimiento.
XGBoost es una biblioteca optimizada de aumento de gradiente distribuido diseñada para ser altamente eficiente, flexible y portátil. Implementa algoritmos de aprendizaje automático bajo el marco de Gradient Boosting. XGBoost proporciona un impulso de árbol paralelo (también conocido como GBDT, GBM) que resuelve muchos problemas de ciencia de datos de forma rápida y precisa. Admite capacitación distribuida en múltiples máquinas, incluidos los clústeres de AWS, GCE, Azure y Yarn. Además, se puede integrar con Flink, Spark y otros sistemas de flujo de datos en la nube.
cuML es un conjunto de bibliotecas que implementan algoritmos de aprendizaje automático y funciones matemáticas primitivas que comparten API compatibles con otros proyectos RAPIDS. cuML permite a los científicos de datos, investigadores e ingenieros de software ejecutar tareas tabulares de ML tradicionales en GPU sin entrar en detalles de la programación CUDA. En la mayoría de los casos, la API Python de cuML coincide con la API de scikit-learn.
Emu es una biblioteca GPGPU para Rust centrada en la portabilidad, la modularidad y el rendimiento. Es una abstracción específica de computación estilo CUDA sobre WebGPU que proporciona una funcionalidad específica para hacer que WebGPU se sienta más como CUDA.
Scalene es un perfilador de CPU, GPU y memoria de alto rendimiento para Python que hace una serie de cosas que otros perfiladores de Python no hacen ni pueden hacer. Ejecuta órdenes de magnitud más rápido que muchos otros generadores de perfiles y, al mismo tiempo, ofrece información mucho más detallada.
MLpack es una biblioteca de aprendizaje automático C++ rápida y flexible escrita en C++ y construida sobre la biblioteca de álgebra lineal Armadillo, la biblioteca de optimización numérica ensmallen y partes de Boost.
Netron es un visor de modelos de redes neuronales, aprendizaje profundo y aprendizaje automático. Es compatible con ONNX, TensorFlow Lite, Caffe, Keras, Darknet, PaddlePaddle, ncnn, MNN, Core ML, RKNN, MXNet, MindSpore Lite, TNN, Barracuda, Tengine, CNTK, TensorFlow.js, Caffe2 y UFF.
Lightning es una herramienta que crea y entrena modelos de PyTorch y los conecta al ciclo de vida de ML utilizando plantillas de aplicaciones Lightning, sin manejar infraestructura de bricolaje, gestión de costos, escalado, etc.
OpenNN es una biblioteca de redes neuronales de código abierto para aprendizaje automático. Contiene algoritmos y utilidades sofisticados para hacer frente a muchas soluciones de inteligencia artificial.
H20 es una plataforma de IA en la nube que resuelve problemas comerciales complejos y acelera el descubrimiento de nuevas ideas con resultados que puede comprender y en los que puede confiar.
Gensim es una biblioteca de Python para modelado de temas, indexación de documentos y recuperación de similitudes con grandes corpus. El público objetivo es la comunidad de procesamiento del lenguaje natural (PNL) y recuperación de información (IR).
llama.cpp es un puerto del modelo LLaMA de Facebook en C/C++.
hmmlearn es un conjunto de algoritmos para el aprendizaje y la inferencia no supervisados de modelos ocultos de Markov.
Nextjournal es un cuaderno para investigaciones reproducibles. Ejecuta todo lo que puedas poner en un contenedor Docker. Mejore su flujo de trabajo con cuadernos políglotas, control de versiones automático y colaboración en tiempo real. Ahorre tiempo y dinero con el aprovisionamiento bajo demanda, incluida la compatibilidad con GPU.
IPython proporciona una rica arquitectura para informática interactiva con:
Veles es una plataforma distribuida para el desarrollo rápido de aplicaciones de aprendizaje profundo actualmente desarrollada por Samsung.
DyNet es una biblioteca de redes neuronales desarrollada por la Universidad Carnegie Mellon y muchas otras. Está escrito en C++ (con enlaces en Python) y está diseñado para ser eficiente cuando se ejecuta en CPU o GPU, y para funcionar bien con redes que tienen estructuras dinámicas que cambian para cada instancia de entrenamiento. Este tipo de redes son particularmente importantes en tareas de procesamiento del lenguaje natural, y DyNet se ha utilizado para construir sistemas de última generación para análisis sintáctico, traducción automática, inflexión morfológica y muchas otras áreas de aplicación.
Ray es un marco unificado para escalar aplicaciones de IA y Python. Consiste en un tiempo de ejecución distribuido central y un conjunto de herramientas de bibliotecas (Ray AIR) para acelerar las cargas de trabajo de ML.
Whisper.cpp es una inferencia de alto rendimiento del modelo de reconocimiento automático de voz (ASR) Whisper de OpenAI.
ChatGPT Plus es un plan de suscripción piloto ( $20/mes ) para ChatGPT, una IA conversacional que puede chatear con usted, responder preguntas de seguimiento y desafiar suposiciones incorrectas.
Auto-GPT es un "agente de IA" que, dado un objetivo en lenguaje natural, puede intentar lograrlo dividiéndolo en subtareas y utilizando Internet y otras herramientas en un bucle automático. Utiliza las API GPT-4 o GPT-3.5 de OpenAI y se encuentra entre los primeros ejemplos de una aplicación que utiliza GPT-4 para realizar tareas autónomas.
Chatbot UI de mckaywrigley es un kit de chatbot avanzado para los modelos de chat de OpenAI creado sobre Chatbot UI Lite usando Next.js, TypeScript y Tailwind CSS. Esta versión de ChatBot UI es compatible con los modelos GPT-3.5 y GPT-4. Las conversaciones se almacenan localmente en su navegador. Puede exportar e importar conversaciones para protegerlas contra la pérdida de datos. Ver una demostración.
Chatbot UI Lite de mckaywrigley es un kit de inicio de chatbot simple para el modelo de chat de OpenAI que utiliza Next.js, TypeScript y Tailwind CSS. Ver una demostración.
MiniGPT-4 es una mejora de la comprensión visión-lenguaje con modelos avanzados de lenguaje grande.
GPT4All es un ecosistema de chatbots de código abierto entrenados en colecciones masivas de datos limpios de asistentes, incluidos códigos, historias y diálogos basados en LLaMa.
GPT4All UI es una aplicación web Flask que proporciona una interfaz de usuario de chat para interactuar con el chatbot GPT4All.
Alpaca.cpp es un modelo rápido similar a ChatGPT localmente en su dispositivo. Combina el modelo básico LLaMA con una reproducción abierta de Stanford Alpaca, un ajuste fino del modelo base para obedecer instrucciones (similar al RLHF utilizado para entrenar ChatGPT) y un conjunto de modificaciones a llama.cpp para agregar una interfaz de chat.
llama.cpp es un puerto del modelo LLaMA de Facebook en C/C++.
OpenPlayground es un campo de juegos para ejecutar modelos similares a ChatGPT localmente en su dispositivo.
Vicuña es un chatbot de código abierto entrenado mediante ajustes LLaMA. Aparentemente logra más del 90% de calidad de chatgpt y entrenarlo cuesta $300.
Yeagar ai es un creador de Langchain Agent diseñado para ayudarlo a crear, crear prototipos e implementar agentes impulsados por IA con facilidad.
Vicuña se crea ajustando un modelo base de LLaMA utilizando aproximadamente 70.000 conversaciones compartidas por usuarios recopiladas de ShareGPT.com con API públicas. Para garantizar la calidad de los datos, convierte el HTML nuevamente en rebajas y filtra algunas muestras inapropiadas o de baja calidad.
ShareGPT es un lugar para compartir tus conversaciones ChatGPT más locas con un solo clic. Con 198.404 conversaciones compartidas hasta el momento.
FastChat es una plataforma abierta para capacitar, atender y evaluar chatbots basados en modelos de lenguaje grandes.
Haystack es un marco de PNL de código abierto para interactuar con sus datos utilizando modelos Transformer y LLM (GPT-4, ChatGPT y similares). Ofrece herramientas listas para producción para crear rápidamente aplicaciones complejas de toma de decisiones, respuesta a preguntas, búsqueda semántica, generación de texto y más.
StableLM (Stability AI Language Models) es una serie de modelos de lenguaje StableLM y se actualizará continuamente con nuevos puntos de control.
Dolly de Databricks es un modelo de lenguaje grande que sigue instrucciones y se entrena en la plataforma de aprendizaje automático de Databricks y que tiene licencia para uso comercial.
GPTCach es una biblioteca para crear caché semántica para consultas LLM.
AlaC es un generador de infraestructura como código de inteligencia artificial.
Adrenaline es una herramienta que te permite hablar con tu código base. Está impulsado por análisis estático, búsqueda vectorial y modelos de lenguaje grandes.
OpenAssistant es un asistente basado en chat que comprende tareas, puede interactuar con sistemas de terceros y recuperar información dinámicamente para hacerlo.
DoctorGPT es un binario autónomo y liviano que monitorea los registros de su aplicación en busca de problemas y los diagnostica.
HttpGPT es un complemento de Unreal Engine 5 que facilita la integración con los servicios basados en GPT de OpenAI (ChatGPT y DALL-E) a través de solicitudes REST asíncronas, lo que facilita a los desarrolladores la comunicación con estos servicios. También incluye herramientas de edición para integrar Chat GPT y generación de imágenes DALL-E directamente en el motor.
PaLM 2 es un modelo de lenguaje grande de próxima generación que se basa en el legado de investigación innovadora de Google en aprendizaje automático e inteligencia artificial responsable. Incluye tareas de razonamiento avanzado, que incluyen código y matemáticas, clasificación y respuesta a preguntas, traducción y dominio multilingüe, y generación de lenguaje natural mejor que nuestros LLM de última generación anteriores.
Med-PaLM es un modelo de lenguaje grande (LLM) diseñado para brindar respuestas de alta calidad a preguntas médicas. Aprovecha el poder de los grandes modelos de lenguaje de Google, que hemos alineado con el ámbito médico con un conjunto de demostraciones de expertos médicos cuidadosamente seleccionadas.
Sec-PaLM es un modelo de lenguaje grande (LLM) que acelera la capacidad de ayudar a las personas responsables de mantener seguras sus organizaciones. Estos nuevos modelos no sólo brindan a las personas una forma más natural y creativa de comprender y gestionar la seguridad.
Volver a la cima
Volver a la cima
Volver a la cima
Localai es una API local compatible con OpenAI, impulsada por la comunidad, impulsada por la comunidad. Reemplazo de entrega para OpenAI Running LLMS en hardware de grado consumidor sin necesidad de GPU. Es una API para ejecutar modelos compatibles con GGML: LLAMA, GPT4All, RWKV, Whisper, Vicuna, Koala, Gpt4all-J, Cerebras, Falcon, Dolly, Starcoder y muchos otros.
Llama.cpp es un modelo de LLAMA de puerto de Facebook en C/C ++.
Ollama es una herramienta para correr con Llama 2 y otros modelos de idiomas grandes localmente.
Localai es una API local compatible con OpenAI, impulsada por la comunidad, impulsada por la comunidad. Reemplazo de entrega para OpenAI Running LLMS en hardware de grado consumidor sin necesidad de GPU. Es una API para ejecutar modelos compatibles con GGML: LLAMA, GPT4All, RWKV, Whisper, Vicuna, Koala, Gpt4all-J, Cerebras, Falcon, Dolly, Starcoder y muchos otros.
Serge es una interfaz web para chatear con Alpaca a través de Llama.cpp. Totalmente autohospedado y dockerized, con una API fácil de usar.
OpenLLM es una plataforma abierta para operar modelos de idiomas grandes (LLM) en producción. Tune, servir, implementar y monitorear cualquier LLM con facilidad.
Llama-GPT es un chatbot con forma de chatgpt egoísta, fuera de línea. Impulsado por Llama 2. 100% privado, sin datos que salen de su dispositivo.
LLAMA2 WebUI es una herramienta para ejecutar cualquier Llama 2 localmente con UI de Gradio en GPU o CPU desde cualquier lugar (Linux/Windows/Mac). Use llama2-wrapper como su backend local LLAMA2 para agentes/aplicaciones generativos.
Llama2.C es una herramienta para entrenar la arquitectura LLAMA 2 LLM en Pytorch y luego inferirla con un simple archivo C de 700 líneas (run.c).
Alpaca.cpp es un modelo rápido similar a ChatGPT localmente en su dispositivo. Combina el modelo de la Fundación LLAMA con una reproducción abierta de Stanford Alpaca, un ajuste del modelo base para obedecer las instrucciones (similar al RLHF utilizado para entrenar chatgpt) y un conjunto de modificaciones a llama.cpp para agregar una interfaz de chat.
GPT4All es un ecosistema de chatbots de código abierto entrenados en una colección masiva de datos de asistente limpio que incluyen código, historias y diálogo basados en LLAMA.
Minigpt-4 es una comprensión en el idioma de visión mejorada con modelos de lenguaje grandes avanzados
Lollms WebUI es un HUB para modelos LLM (modelo de lenguaje grande). Su objetivo es proporcionar una interfaz fácil de usar para acceder y utilizar varios modelos LLM para una amplia gama de tareas. Ya sea que necesite ayuda para escribir, codificar, organizar datos, generar imágenes o buscar respuestas a sus preguntas.
LM Studio es una herramienta para descubrir, descargar y ejecutar LLM locales.
La interfaz de usuario web de Gradio es una herramienta para modelos de idiomas grandes. Admite Transformers, GPTQ, Llama.CPP (GGML/GGUF), modelos LLAMA.
OpenPlay Ground es un juego de juego para ejecutar modelos similares a ChatGPT localmente en su dispositivo.
Vicuna es un chatbot de código abierto entrenado por Fine Tuning Llama. Aparentemente logra más del 90% de calidad de chatgpt y cuesta $ 300 para entrenar.
Yeagar AI es un creador de agentes de Langchain diseñado para ayudarlo a construir, prototipos y implementar agentes con facilidad con facilidad.
Koboldcpp es un software de generación de texto de IA fácil de usar para los modelos GGML. Es una sola distribución de Concedo, que se basa en Llama.cpp, y agrega un punto final versátil de la API de Kobold, soporte de formato adicional, compatibilidad con atraso, así como una interfaz de usuario elegante con historias persistentes, herramientas de edición, formatos de guardado, memoria, mundo. Información, nota del autor, personajes y escenarios.
Volver a la cima
Fuzzy Logic es un enfoque heurístico que permite un procesamiento más avanzado del árbol de decisiones y una mejor integración con la programación basada en reglas.
Arquitectura de un sistema lógico difuso. Fuente: Researchgate
Support Vector Machine (SVM) es un modelo de aprendizaje automático supervisado que utiliza algoritmos de clasificación para problemas de clasificación de dos grupos.
Máquina de vectores de soporte (SVM). Fuente: OpenClipart
Las redes neuronales son un subconjunto de aprendizaje automático y están en el corazón de los algoritmos de aprendizaje profundo. El nombre/estructura está inspirado en el cerebro humano que copia el proceso que las neuronas/nodos biológicos se señalan entre sí.
Red neuronal profunda. Fuente: IBM
Las redes neuronales convolucionales (R-CNN) es un algoritmo de detección de objetos que primero segmenta la imagen para encontrar posibles cuadros limitados relevantes y luego ejecutar el algoritmo de detección para encontrar la mayoría de los objetos probables en esos cuadros delimitadores.
Redes neuronales convolucionales. Fuente: CS231N
Las redes neuronales recurrentes (RNN) es un tipo de red neuronal artificial que utiliza datos secuenciales o datos de series de tiempo.
Redes neuronales recurrentes. Fuente: Slideteam
Los perceptrones multicapa (MLP) son redes neuronales de múltiples capas compuestas de múltiples capas de perceptrones con una activación umbral.
Perceptrones de múltiples capas. Fuente: Deepai
El bosque aleatorio es un algoritmo de aprendizaje automático de uso común, que combina la producción de múltiples árboles de decisión para alcanzar un solo resultado. No se puede podar un árbol de decisión en un bosque para el muestreo y, por lo tanto, la selección de predicción. Su facilidad de uso y flexibilidad ha alimentado su adopción, ya que maneja los problemas de clasificación y regresión.
Bosque aleatorio. Fuente: Wikimedia
Los árboles de decisión son modelos estructurados en árboles para la clasificación y la regresión.
** Árboles de decisión. Fuente: CMU
Naive Bayes es un algoritmo de aprendizaje automático que se utiliza problemas de clasificación de cal. Se basa en la aplicación del teorema de Bayes con fuertes supuestos de independencia entre las características.
Teorema de Bayes. Fuente: Mathisfun
Volver a la cima
Pytorch es un marco de aprendizaje profundo de código abierto que acelera el camino de la investigación a la producción, utilizado para aplicaciones como la visión por computadora y el procesamiento del lenguaje natural. Pytorch es desarrollado por el laboratorio de investigación de IA de Facebook.
Empezando con Pytorch
Documentación de Pytorch
Foro de discusión de Pytorch
Top Pytorch Courses Online | Cursera
Top Pytorch Courses Online | Udemy
Aprenda Pytorch con cursos y clases en línea | edX
Fundamentos de Pytorch - Learn | Microsoft Docs
Introducción al aprendizaje profundo con Pytorch | Udacidad
Desarrollo de Pytorch en Código Visual Studio
Pytorch on Azure - Aprendizaje profundo con Pytorch | MicrosoftAzure
Pytorch - Azure Databricks | Microsoft Docs
Aprendizaje profundo con Pytorch | Servicios web de Amazon (AWS)
Comenzando con Pytorch en Google Cloud
Pytorch Mobile es un flujo de trabajo ML de extremo a extremo desde la capacitación hasta la implementación para dispositivos móviles iOS y Android.
Torchscript es una forma de crear modelos serializables y optimizables a partir del código Pytorch. Esto permite que cualquier programa Torchscript se guarde en un proceso de Python y se cargue en un proceso donde no hay dependencia de Python.
Torchserve es una herramienta flexible y fácil de usar para servir modelos Pytorch.
Keras es una API de redes neuronales de alto nivel, escrita en Python y capaz de funcionar sobre TensorFlow, CNTK o Theano.In se desarrolló con un enfoque en habilitar la experimentación rápida. Es capaz de ejecutarse sobre TensorFlow, Microsoft Cognitive Toolkit, R, Thano o PlaidMl.
El tiempo de ejecución de ONNX es un acelerador multiplataforma de inferencia y entrenamiento de ML de alto rendimiento. Admite modelos de marcos de aprendizaje profundo como Pytorch y TensorFlow/Keras, así como bibliotecas clásicas de aprendizaje automático como Scikit-Learn, LightGBM, XGBOost, etc.
Kornia es una biblioteca de visión por computadora diferenciable que consiste en un conjunto de rutinas y módulos diferenciables para resolver problemas genéricos de CV (visión por computadora).
Pytorch-NLP es una biblioteca para el procesamiento del lenguaje natural (NLP) en Python. Está construido con la última investigación en mente, y fue diseñado desde el primer día para apoyar la prototipos rápidos. Pytorch-NLP viene con incrustaciones previamente capacitadas, muestreadores, cargadores de conjuntos de datos, métricas, módulos de red neuronales y codificadores de texto.
Ignite es una biblioteca de alto nivel para ayudar con la capacitación y la evaluación de las redes neuronales en Pytorch de manera flexible y transparente.
Hummingbird es una biblioteca para compilar modelos ML tradicionales entrenados en cálculos de tensor. Permite a los usuarios aprovechar a la perfección los marcos de redes neuronales (como Pytorch) para acelerar los modelos ML tradicionales.
Deep Graph Library (DGL) es un paquete de Python creado para una fácil implementación de la familia de modelos de red neuronal Graph, encima de Pytorch y otros marcos.
Tensorly es una API de alto nivel para métodos de tensor y redes neuronales tensorizadas profundas en Python que tiene como objetivo simplificar el aprendizaje tensor.
GpyTorch es una biblioteca de procesos gaussiana implementada con Pytorch, diseñada para crear modelos de procesos gaussianos escalables y flexibles.
Poutyne es un marco similar a Keras para Pytorch y maneja gran parte del código básico necesario para entrenar redes neuronales.
Forte es un kit de herramientas para construir tuberías NLP con componentes compuestos, interfaces de datos convenientes e interacción de tarea cruzada.
Torchmetrics es una métrica de aprendizaje automático para aplicaciones de Pytorch escalables distribuidas.
Captum es una biblioteca de código abierto y extensible para la interpretabilidad del modelo construida en Pytorch.
Transformer es un procesamiento de lenguaje natural de última generación para Pytorch, TensorFlow y Jax.
Hydra es un marco para configurar elegantemente aplicaciones complejas.
Accelerate es una forma simple de entrenar y usar modelos Pytorch con Multi-GPU, TPU, precisión mixta.
Ray es un marco rápido y simple para construir y ejecutar aplicaciones distribuidas.
Parlai es una plataforma unificada para compartir, capacitar y evaluar modelos de diálogo en muchas tareas.
Pytorchvideo es una biblioteca de aprendizaje profundo para la investigación de comprensión de video. Alojan varios modelos, conjuntos de datos, tuberías de entrenamiento y más centrados en videos.
Opacus es una biblioteca que permite capacitar modelos de Pytorch con privacidad diferencial.
Pytorch Lightning es una biblioteca ML similar a Keras para Pytorch. Le deja la lógica de entrenamiento y validación del núcleo y automatiza el resto.
Pytorch geométrico temporal es una biblioteca de extensión temporal (dinámica) para la geométrica de Pytorch.
Pytorch geométrico es una biblioteca para el aprendizaje profundo en datos de entrada irregulares, como gráficos, nubes de puntos y colectores.
Raster Vision es un marco de código abierto para el aprendizaje profundo en imágenes satelitales e aéreas.
Crypten es un marco para la privacidad de la preservación de ML. Su objetivo es hacer que las técnicas de computación seguras sean accesibles para los practicantes de ML.
Optuna es un marco de optimización de hiperparameter de código abierto para automatizar la búsqueda de hiperparameter.
Pyro es un lenguaje de programación probabilístico universal (PPL) escrito en Python y con el apoyo de Pytorch en el backend.
Albumentations es una biblioteca de aumento de imagen rápida y extensible para diferentes tareas CV como clasificación, segmentación, detección de objetos y estimación de pose.
Skorch es una biblioteca de alto nivel para Pytorch que proporciona compatibilidad completa de Scikit-Learn.
MMF es un marco modular para la investigación multimodal de visión e idiomas de Facebook AI Research (Fair).
ADAPTDL es un marco de capacitación y programación de aprendizaje profundo adaptativo a recursos.
Polyaxon es una plataforma para construir, capacitar y monitorear aplicaciones de aprendizaje profundo a gran escala.
TextBrewer es un kit de herramientas de destilación de conocimiento basado en Pytorch para el procesamiento del lenguaje natural
Advertorch es una caja de herramientas para la investigación de robustez adversa. Contiene módulos para generar ejemplos adversos y defender contra ataques.
NEMO es un kit de herramientas AA para IA conversacional.
Clinicadl es un marco para la clasificación reproducible de la enfermedad de Alzheimer
Las líneas de base estables3 (SB3) es un conjunto de implementaciones confiables de algoritmos de aprendizaje de refuerzo en Pytorch.
Torchio es un conjunto de herramientas para leer, preprocesar, probar, aumentar y escribir imágenes médicas 3D en aplicaciones de aprendizaje profundo escritas en Pytorch.
Pysyft es una biblioteca de Python para el aprendizaje profundo encriptado y de privacidad.
Flair es un marco muy simple para el procesamiento del lenguaje natural (PNL) de última generación.
GLOW es un compilador ML que acelera el rendimiento de los marcos de aprendizaje profundo en diferentes plataformas de hardware.
Fairscale es una biblioteca de extensión de Pytorch para entrenamiento de alto rendimiento y gran escala en una o múltiples máquinas/nodos.
Monai es un marco de aprendizaje profundo que proporciona capacidades fundamentales optimizadas por el dominio para desarrollar flujos de trabajo de capacitación de imágenes en salud.
PFRL es una biblioteca de aprendizaje de refuerzo profundo que implementa varios algoritmos de refuerzo profundo de última generación en Python usando Pytorch.
Einops es una operación tensor flexible y potente para un código legible y confiable.
Pytorch3d es una biblioteca de aprendizaje profundo que proporciona componentes eficientes y reutilizables para la investigación de visión por computadora 3D con Pytorch.
Ensemble Pytorch es un marco de conjunto unificado para Pytorch para mejorar el rendimiento y la robustez de su modelo de aprendizaje profundo.
Ligeramente es un marco de visión por computadora para el aprendizaje auto-supervisado.
Higher es una biblioteca que facilita la implementación de algoritmos de meta-aprendizaje basados en gradientes arbitrariamente complejos y bucles de optimización anidados con pytorch casi anidadores.
Horovod es una biblioteca de capacitación distribuida para marcos de aprendizaje profundo. Horovod tiene como objetivo hacer que el DL distribuido sea rápido y fácil de usar.
Pennylane es una biblioteca de ML cuántica, diferenciación automática y optimización de cálculos clásicos cuánticos híbridos.
Detectron2 es la plataforma de próxima generación de Fair para la detección y segmentación de objetos.
Fastai es una biblioteca que simplifica las redes neuronales rápidas y precisas de la capacitación utilizando las mejores prácticas modernas.
Volver a la cima
TensorFlow es una plataforma de código abierto de extremo a extremo para el aprendizaje automático. Tiene un ecosistema integral y flexible de herramientas, bibliotecas y recursos comunitarios que permite a los investigadores impulsar el estado del arte en ML y los desarrolladores construyen e implementan fácilmente aplicaciones alimentadas con ML.
Comenzando con TensorFlow
Tutoriales de tensorflow
Certificado de desarrollador de TensorFlow | TensorFlow
Comunidad de TensorFlow
Modelos y conjuntos de datos de tensorflow
Nube tensorflow
Educación de aprendizaje automático | TensorFlow
Top TensorFlow Cours en línea | Cursera
Top TensorFlow Cours en línea | Udemy
Aprendizaje profundo con TensorFlow | Udemy
Aprendizaje profundo con TensorFlow | edX
Introducción a TensorFlow para el aprendizaje profundo | Udacidad
Introducción a TensorFlow: curso de bloqueo de aprendizaje automático | Desarrolladores de Google
Entrenar e implementar un modelo TensorFlow - Azure Machine Learning
Aplicar modelos de aprendizaje automático en funciones de Azure con Python y TensorFlow | MicrosoftAzure
Aprendizaje profundo con TensorFlow | Servicios web de Amazon (AWS)
Tensorflow - Amazon EMR | Documentación de AWS
TensorFlow Enterprise | Nube de Google
Tensorflow Lite es un marco de aprendizaje profundo de código abierto para implementar modelos de aprendizaje automático en dispositivos móviles y IoT.
Tensorflow.js es una biblioteca de JavaScript que le permite desarrollar o ejecutar modelos ML en JavaScript, y usar ML directamente en el lado del cliente del navegador, lado del servidor a través de Node.js, móvil nativo a través de React Native, Desktop nativo a través de Electron e incluso en IoT Dispositivos a través de Node.js en Raspberry Pi.
Tensorflow_macos es una versión optimizada MAC de TensorFlow y TensorFlow Addons para MacOS 11.0+ acelerado utilizando el marco de cómputo ML de Apple.
Google Colaboratory es un entorno de cuaderno Jupyter gratuito que no requiere configuración y se ejecuta completamente en la nube, lo que le permite ejecutar el código TensorFlow en su navegador con un solo clic.
La herramienta de WHIF IF es una herramienta para el sondeo sin código de modelos de aprendizaje automático, útil para la comprensión del modelo, la depuración y la equidad. Disponible en TensorBoard y Jupyter o cuadernos Colab.
TensorBoard es un conjunto de herramientas de visualización para comprender, depurar y optimizar los programas de TensorFlow.
Keras es una API de redes neuronales de alto nivel, escrita en Python y capaz de funcionar sobre TensorFlow, CNTK o Theano.In se desarrolló con un enfoque en habilitar la experimentación rápida. Es capaz de ejecutarse sobre TensorFlow, Microsoft Cognitive Toolkit, R, Thano o PlaidMl.
XLA (álgebra lineal acelerada) es un compilador específico de dominio para álgebra lineal que optimiza los cálculos de flujo de tensor. Los resultados son mejoras en la velocidad, el uso de la memoria y la portabilidad en las plataformas de servidor y móviles.
ML PERF es una amplia suite de referencia ML para medir el rendimiento de los marcos de software ML, los aceleradores de hardware ML y las plataformas ML en la nube.
Tensorflow Playground es un entorno de desarrollo para jugar con una red neuronal en su navegador.
TPU Research Cloud (TRC) es un programa permite a los investigadores solicitar acceso a un clúster de más de 1,000 TPU de nubes sin cargo para ayudarlos a acelerar la próxima ola de avances de investigación.
MLIR es un nuevo marco de representación intermedia y compilador.
La red es una biblioteca para soluciones ML flexibles, controladas e interpretables con limitaciones de forma de sentido común.
Tensorflow Hub es una biblioteca para el aprendizaje automático reutilizable. Descargue y reutilice los últimos modelos capacitados con una cantidad mínima de código.
Tensorflow Cloud es una biblioteca para conectar su entorno local a Google Cloud.
TensorFlow Model Optimization Toolkit es un conjunto de herramientas para optimizar modelos ML para la implementación y ejecución.
TensorFlow Recomenders es una biblioteca para construir modelos de sistemas de recomendación.
El texto de TensorFlow es una colección de clases y operaciones relacionadas con NLP y OPS listos para usar con TensorFlow 2.
TensorFlow Graphics es una biblioteca de funcionalidades de gráficos de computadora que van desde cámaras, luces y materiales hasta renderizadores.
TensorFlow Federated es un marco de código abierto para el aprendizaje automático y otros cálculos en datos descentralizados.
La probabilidad de TensorFlow es una biblioteca para el razonamiento probabilístico y el análisis estadístico.
Tensor2Tensor es una biblioteca de modelos de aprendizaje profundo y conjuntos de datos diseñados para hacer que el aprendizaje profundo sea más accesible y acelerar la investigación de ML.
TensorFlow Privacy es una biblioteca de Python que incluye implementaciones de optimizadores de TensorFlow para capacitar a modelos de aprendizaje automático con privacidad diferencial.
TensorFlow Ranking es una biblioteca para el aprendizaje de las técnicas de clasificar (LTR) en la plataforma TensorFlow.
TensorFlow Agents es una biblioteca para el aprendizaje de refuerzo en TensorFlow.
TensorFlow Addons es un depósito de contribuciones que se ajustan a patrones de API bien establecidos, pero implementan una nueva funcionalidad no disponible en TensorFlow central, mantenida por SIG Addons. TensorFlow es de forma nativa que admite una gran cantidad de operadores, capas, métricas, pérdidas y optimizadores.
TensorFlow E/S es un conjunto de datos, transmisión y extensiones del sistema de archivos, mantenidas por Sig Io.
TensorFlow Quantum es una biblioteca de aprendizaje automático cuántico para la prototipos rápidos de modelos ML de clásicos cuánticos híbridos.
La dopamina es un marco de investigación para la prototipos rápidos de los algoritmos de aprendizaje de refuerzo.
TRFL es una biblioteca para los bloques de construcción de aprendizaje de refuerzo creados por DeepMind.
Mesh TensorFlow es un idioma para el aprendizaje profundo distribuido, capaz de especificar una amplia clase de cálculos de tensor distribuidos.
RaggedTensors es una API que facilita almacenar y manipular datos con forma no uniforme, incluidos texto (palabras, oraciones, caracteres) y lotes de longitud variable.
Unicode OPS es una API que admite trabajar con texto Unicode directamente en TensorFlow.
Magenta es un proyecto de investigación que explora el papel del aprendizaje automático en el proceso de creación de arte y música.
Nucleus es una biblioteca de código Python y C ++ diseñado para facilitar la lectura, escribir y analizar datos en formatos de archivo genómicos comunes como SAM y VCF.
Sonnet es una biblioteca de DeepMind para construir redes neuronales.
El aprendizaje estructurado neural es un marco de aprendizaje para capacitar a las redes neuronales aprovechando señales estructuradas además de las entradas de entradas.
La remediación del modelo es una biblioteca para ayudar a crear y entrenar modelos de una manera que reduzca o elimine el daño del usuario resultante de los sesgos de rendimiento subyacentes.
Los indicadores de equidad es una biblioteca que permite el cálculo fácil de métricas de equidad comúnmente identificadas para clasificadores binarios y multiclase.
Decision Forests es un algoritmos de última generación para capacitar, servir e interpretar modelos que utilizan bosques de decisión para la clasificación, regresión y clasificación.
Volver a la cima
Core ML es un marco de Apple para integrar modelos de aprendizaje automático en aplicaciones que se ejecutan en dispositivos Apple (incluidos iOS, WatchOS, MacOS y TVOS). Core ML introduce un formato de archivo público (.mlmodel) para un amplio conjunto de métodos ML que incluyen redes neuronales profundas (tanto convolucionales como recurrentes), conjuntos de árboles con impulso y modelos lineales generalizados. Los modelos en este formato se pueden integrar directamente en las aplicaciones a través de Xcode.
Introducción a Core ML
Integrando un modelo ML central en su aplicación
Modelos ML Core ML
Referencia de API de Core ML
Especificación ML del núcleo
Foros de desarrolladores de Apple para Core ML
Cursos ML de Top Core en línea | Udemy
Cursos ML de Top Core en línea | Cursera
IBM Watson Services para Core ML | IBM
Genere activos ML Core utilizando IBM Maximo Visual Inspection | IBM
Core ML Tools es un proyecto que contiene herramientas de soporte para la conversión, edición y validación del modelo Core ML.
Create ML es una herramienta que proporciona nuevas formas de capacitar a los modelos de aprendizaje automático en su Mac. Saca la complejidad del entrenamiento modelo mientras produce modelos ML centrales potentes.
Tensorflow_macos es una versión optimizada MAC de TensorFlow y TensorFlow Addons para MacOS 11.0+ acelerado utilizando el marco de cómputo ML de Apple.
Apple Vision es un marco que realiza la detección de puntos de referencia de cara y cara, detección de texto, reconocimiento de códigos de barras, registro de imágenes y seguimiento de características generales. La visión también permite el uso de modelos ML personalizados para tareas como la clasificación o la detección de objetos.
Keras es una API de redes neuronales de alto nivel, escrita en Python y capaz de funcionar sobre TensorFlow, CNTK o Theano.In se desarrolló con un enfoque en habilitar la experimentación rápida. Es capaz de ejecutarse sobre TensorFlow, Microsoft Cognitive Toolkit, R, Thano o PlaidMl.
XGBOOST es una biblioteca de impulso de gradiente distribuido optimizado diseñada para ser altamente eficiente, flexible y portátil. Implementa algoritmos de aprendizaje automático en el marco de impulso de gradiente. XGBOOST proporciona un refuerzo de árbol paralelo (también conocido como GBDT, GBM) que resuelve muchos problemas de ciencia de datos de una manera rápida y precisa. Admite capacitación distribuida en múltiples máquinas, incluidos los grupos de AWS, GCE, Azure e hilo. Además, se puede integrar con Flink, Spark y otros sistemas de flujo de datos en la nube.
LibSVM es un software integrado para la clasificación de vectores de soporte, (C-SVC, NU-SVC), regresión (Epsilon-SVR, NU-SVR) y estimación de distribución (SVM de una clase). Admite la clasificación de múltiples clases.
Scikit-Learn es una herramienta simple y eficiente para la minería de datos y el análisis de datos. Está construido en Numpy, Scipy y Mathplotlib.
Xcode incluye todo lo que los desarrolladores necesitan para crear excelentes aplicaciones para Mac, iPhone, iPad, Apple TV y Apple Watch. XCode proporciona a los desarrolladores un flujo de trabajo unificado para el diseño de la interfaz de usuario, la codificación, las pruebas y la depuración. Xcode se construye como una aplicación universal que se ejecuta 100% de forma nativa en CPU basadas en Intel y Apple Silicon. Incluye un SDK de MacOS unificado que presenta todos los marcos, compiladores, depuradores y otras herramientas que necesita para crear aplicaciones que se ejecuten de forma nativa en Apple Silicon y la CPU Intel X86_64.
Swiftui es un conjunto de herramientas de interfaz de usuario que proporciona vistas, controles y estructuras de diseño para declarar la interfaz de usuario de su aplicación. El marco Swiftui proporciona controladores de eventos para entregar grifos, gestos y otros tipos de información a su aplicación.
UIKIT es un marco que proporciona la infraestructura requerida para sus aplicaciones iOS o TVOS. Proporciona la arquitectura de la ventana y la vista para implementar su interfaz, la infraestructura de manejo de eventos para entregar múltiples toque y otros tipos de información a su aplicación, y el bucle de ejecución principal necesario para administrar las interacciones entre el usuario, el sistema y su aplicación.
AppKit es un kit de herramientas de interfaz de usuario gráfico que contiene todos los objetos que necesita para implementar la interfaz de usuario para una aplicación MacOS como Windows, paneles, botones, menús, desplazadores y campos de texto, y maneja todos los detalles para usted, ya que es eficiente. Dibuja en la pantalla, se comunica con dispositivos de hardware y buffers de pantalla, borra las áreas de la pantalla antes de dibujar y clips vistas.
ARKIT es un conjunto de herramientas de desarrollo de software para permitir a los desarrolladores crear aplicaciones de realidad aumentada para iOS desarrollados por Apple. La última versión ARKIT 3.5 aprovecha el nuevo escáner LiDAR y el sistema de detección de profundidad en iPad Pro (2020) para admitir una nueva generación de aplicaciones AR que usan geometría de escenas para una mejor comprensión de la escena y oclusión de objetos.
RealityKit es un marco para implementar la simulación 3D de alto rendimiento y la representación con la información proporcionada por el marco ARKIT para integrar a la perfección los objetos virtuales en el mundo real.
ScineKit es un marco de gráficos 3D de alto nivel que lo ayuda a crear escenas y efectos animados en 3D en sus aplicaciones iOS.
Instruments es una herramienta de análisis y análisis de rendimiento potente y flexible que forma parte del conjunto de herramientas Xcode. Está diseñado para ayudarlo a perfilar sus aplicaciones, procesos y dispositivos iOS, WatchOS, TVOS y MacOS para comprender y optimizar mejor su comportamiento y rendimiento.
Cocoapods es un administrador de dependencia para Swift y Objective-C utilizado en proyectos XCode especificando las dependencias para su proyecto en un archivo de texto simple. Cocoapods luego resuelve recursivamente las dependencias entre las bibliotecas, obtiene el código fuente para todas las dependencias y crea y mantiene un espacio de trabajo XCode para construir su proyecto.
AppCode está monitoreando constantemente la calidad de su código. Le advierte errores y olores y sugiere fiebres rápidas para resolverlos automáticamente. AppCode proporciona muchas inspecciones de código para Objective-C, Swift, C/C ++ y varias inspecciones de código para otros idiomas compatibles.
Volver a la cima
El aprendizaje profundo es un subconjunto de aprendizaje automático, que es esencialmente una red neuronal con tres o más capas. Sin embargo, estas redes neuronales intentan simular el comportamiento del cerebro humano, lejos de igualar su capacidad. Esto permite que las redes neuronales "aprendan" de grandes cantidades de datos. El aprendizaje puede ser supervisado, semi-supervisado o sin supervisión.
Cursos en línea de aprendizaje profundo | Nvidia
Cursos de aprendizaje profundo en línea en línea | Cursera
Cursos de aprendizaje profundo en línea en línea | Udemy
Aprenda el aprendizaje profundo con cursos y lecciones en línea | edX
Aprendizaje profundo Curso en línea Nanodegree | Udacidad
Curso de aprendizaje automático por Andrew Ng | Cursera
Curso de ingeniería de aprendizaje automático para producción (MLOPS) por Andrew Ng | Cursera
Ciencia de datos: aprendizaje profundo y redes neuronales en Python | Udemy
Comprender el aprendizaje automático con Python | Pluralvista
Cómo pensar en los algoritmos de aprendizaje automático | Pluralvista
Cursos de aprendizaje profundo | Stanford en línea
Aprendizaje profundo - UW Professional y Educación continua
Cursos en línea de aprendizaje profundo | Universidad de Harvard
Cursos de aprendizaje automático para todos | Campamento de datos
Curso de expertos de inteligencia artificial: Edición Platinum | Udemy
Top Cursos de inteligencia artificial en línea | Cursera
Aprenda inteligencia artificial con cursos y lecciones en línea | edX
Certificado profesional en informática para inteligencia artificial | edX
Programa de Nanodegrado de Inteligencia Artificial
Cursos en línea de inteligencia artificial (AI) | Udacidad
Introducción al curso de inteligencia artificial | Udacidad
Edge AI para el curso de desarrolladores de IoT | Udacidad
Razonamiento: árboles de objetivos y sistemas expertos basados en reglas | OpenCourseWare del MIT
Sistemas expertos e inteligencia artificial aplicada
Sistemas autónomos - Microsoft AI
Introducción a Microsoft Project Bonsai
Enseñanza a máquina con la plataforma de sistemas autónomos de Microsoft
Capacitación autónoma de sistemas marítimos | Búsqueda de AMC
Los principales cursos de autos autónomos en línea | Udemy
Sistemas de control aplicado 1: autos autónomos: matemáticas + PID + MPC | Udemy
Aprenda robótica autónoma con cursos y lecciones en línea | edX
Programa de Nanodegrado de Inteligencia Artificial
Cursos y programas en línea de sistemas autónomos | Udacidad
Edge AI para el curso de desarrolladores de IoT | Udacidad
Sistemas autónomos MOOC y cursos en línea gratuitos | Lista de MOOC
Programa de posgrado de robótica y sistemas autónomos | Standford en línea
Laboratorio de sistemas autónomos móviles | OpenCourseWare del MIT
Nvidia Cudnn es una biblioteca de primitivas aceleradas por GPU para redes neuronales profundas. CUDNN proporciona implementaciones altamente sintonizadas para rutinas estándar, como la convolución hacia adelante y hacia atrás, la agrupación, la normalización y las capas de activación. CUDNN acelera los marcos de aprendizaje profundo ampliamente utilizados, incluidos Caffe2, Chainer, Keras, Matlab, MXNet, Pytorch y Tensorflow.
NVIDIA DLSS (Deep Learning Super Muesting) es una tecnología de representación de IA de reducción de imagen temporal que aumenta el rendimiento de los gráficos utilizando procesadores de IA de tensor dedicados en GPU GeForce RTX ™. DLSS utiliza el poder de una red neuronal de aprendizaje profundo para aumentar las velocidades de cuadros y generar imágenes hermosas y nítidas para sus juegos.
AMD FidelityFX Super Resolution (FSR) es una solución de código abierto y de alta calidad para producir marcos de alta resolución a partir de entradas de menor resolución. Utiliza una colección de algoritmos de aprendizaje profundo de vanguardia con un énfasis particular en la creación de bordes de alta calidad, lo que brinda grandes mejoras de rendimiento en comparación con la representación de la resolución nativa directamente. FSR permite "rendimiento práctico" para operaciones de renderización costosas, como el trazado de rayos de hardware para las arquitecturas AMD RDNA ™ y AMD RDNA ™ 2.
Intel Xe Super Sample (Xess) es una tecnología de representación de IA de escala de imagen temporal que aumenta el rendimiento de los gráficos similar al DLSS de NVIDIA (Deep Learning Super Sampling). La arquitectura ARC GPU de Intel (principios de 2022) tendrá GPU que cuentan con Xe-Core dedicados para ejecutar Xess. Las GPU tendrán motores de Matriz de Extentaciones de Matriz XE (XMX) para el procesamiento de IA acelerado por hardware. Xess podrá ejecutarse en dispositivos sin XMX, incluidos los gráficos integrados, sin embargo, el rendimiento de Xess será más bajo en las tarjetas de gráficos que no son inteligentes porque se alimentará con la instrucción DP4A.
Jupyter Notebook es una aplicación web de código abierto que le permite crear y compartir documentos que contienen código en vivo, ecuaciones, visualizaciones y texto narrativo. Jupyter se usa ampliamente en industrias que realizan la limpieza y transformación de datos, simulación numérica, modelado estadístico, visualización de datos, ciencia de datos y aprendizaje automático.
Apache Spark es un motor de análisis unificado para el procesamiento de datos a gran escala. Proporciona API de alto nivel en Scala, Java, Python y R, y un motor optimizado que admite gráficos de cálculo generales para el análisis de datos. También es compatible con un conjunto rico de herramientas de nivel superior que incluye Spark SQL para SQL y Dataframes, MLLIB para el aprendizaje automático, GRAPHX para el procesamiento de gráficos y la transmisión estructurada para el procesamiento de flujo.
El conector Apache Spark para SQL Server y Azure SQL es un conector de alto rendimiento que le permite usar datos transaccionales en análisis de big data y persiste los resultados para consultas o informes ad-hoc. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Instalar. Principios. Escalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
AutoGluon is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Weka is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Microsoft Project Bonsai is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
ROS/ROS2 bridge for CARLA(package) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Robotics Toolbox™ is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Predictive Maintenance Toolbox™ is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird's-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Navigation Toolbox™ is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
Volver a la cima
Reinforcement Learning is a subset of machine learning, which is a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from a process in which a model learns to become more accurate for performing an action in an environment based on feedback in order to maximize the reward. The Learning can be supervised, semi-supervised or unsupervised.
Top Reinforcement Learning Courses | Coursera
Top Reinforcement Learning Courses | Udemy
Top Reinforcement Learning Courses | Udacidad
Reinforcement Learning Courses | Stanford Online
Deep Learning Online Courses | Nvidia
Top Deep Learning Courses Online | Coursera
Top Deep Learning Courses Online | Udemy
Learn Deep Learning with Online Courses and Lessons | edX
Deep Learning Online Course Nanodegree | Udacidad
Machine Learning Course by Andrew Ng | Coursera
Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
Data Science: Deep Learning and Neural Networks in Python | Udemy
Understanding Machine Learning with Python | Pluralvista
How to Think About Machine Learning Algorithms | Pluralvista
Deep Learning Courses | Stanford Online
Deep Learning - UW Professional & Continuing Education
Deep Learning Online Courses | Universidad de Harvard
Machine Learning for Everyone Courses | Campamento de datos
Artificial Intelligence Expert Course: Platinum Edition | Udemy
Top Artificial Intelligence Courses Online | Coursera
Learn Artificial Intelligence with Online Courses and Lessons | edX
Professional Certificate in Computer Science for Artificial Intelligence | edX
Artificial Intelligence Nanodegree program
Artificial Intelligence (AI) Online Courses | Udacidad
Intro to Artificial Intelligence Course | Udacidad
Edge AI for IoT Developers Course | Udacidad
Reasoning: Goal Trees and Rule-Based Expert Systems | OpenCourseWare del MIT
Expert Systems and Applied Artificial Intelligence
Autonomous Systems - Microsoft AI
Introduction to Microsoft Project Bonsai
Machine teaching with the Microsoft Autonomous Systems platform
Autonomous Maritime Systems Training | AMC Search
Top Autonomous Cars Courses Online | Udemy
Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
Learn Autonomous Robotics with Online Courses and Lessons | edX
Artificial Intelligence Nanodegree program
Autonomous Systems Online Courses & Programs | Udacidad
Edge AI for IoT Developers Course | Udacidad
Autonomous Systems MOOC and Free Online Courses | MOOC List
Robotics and Autonomous Systems Graduate Program | Standford Online
Mobile Autonomous Systems Laboratory | OpenCourseWare del MIT
OpenAI is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API.
ReinforcementLearning.jl is a collection of tools for doing reinforcement learning research in Julia.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
AWS RoboMaker is a service that provides a fully-managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Instalar. Principios. Escalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
AutoGluon is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Weka is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Microsoft Project Bonsai is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
ROS/ROS2 bridge for CARLA(package) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Robotics Toolbox™ is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Predictive Maintenance Toolbox™ is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird's-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
Navigation Toolbox™ is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
Volver a la cima
Computer Vision is a field of Artificial Intelligence (AI) that focuses on enabling computers to identify and understand objects and people in images and videos.
OpenCV Courses
Exploring Computer Vision in Microsoft Azure
Top Computer Vision Courses Online | Coursera
Top Computer Vision Courses Online | Udemy
Learn Computer Vision with Online Courses and Lessons | edX
Computer Vision and Image Processing Fundamentals | edX
Introduction to Computer Vision Courses | Udacidad
Computer Vision Nanodegree program | Udacidad
Machine Vision Course |MIT Open Courseware
Computer Vision Training Courses | NobleProg
Visual Computing Graduate Program | Stanford Online
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Microsoft Computer Vision Recipes is a project that provides examples and best practice guidelines for building computer vision systems. This allows people to build a comprehensive set of tools and examples that leverage recent advances in Computer Vision algorithms, neural architectures, and operationalizing such systems. Creatin from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird's-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
LRSLibrary is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Statistics and Machine Learning Toolbox™ is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
Partial Differential Equation Toolbox™ is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Data Acquisition Toolbox™ is a tool that provides apps and functions for configuring data acquisition hardware, reading data into MATLAB® and Simulink®, and writing data to DAQ analog and digital output channels. The toolbox supports a variety of DAQ hardware, including USB, PCI, PCI Express®, PXI®, and PXI Express® devices, from National Instruments® and other vendors.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
Volver a la cima
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) focused on giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics rule-based modeling of human language with statistical, machine learning, and deep learning models.
Natural Language Processing With Python's NLTK Package
Cognitive Services—APIs for AI Developers | MicrosoftAzure
Artificial Intelligence Services - Amazon Web Services (AWS)
Google Cloud Natural Language API
Top Natural Language Processing Courses Online | Udemy
Introduction to Natural Language Processing (NLP) | Udemy
Top Natural Language Processing Courses | Coursera
Natural Language Processing | Coursera
Natural Language Processing in TensorFlow | Coursera
Learn Natural Language Processing with Online Courses and Lessons | edX
Build a Natural Language Processing Solution with Microsoft Azure | Pluralvista
Natural Language Processing (NLP) Training Courses | NobleProg
Natural Language Processing with Deep Learning Course | Standford Online
Advanced Natural Language Processing - MIT OpenCourseWare
Certified Natural Language Processing Expert Certification | IABAC
Natural Language Processing Course - Intel
Natural Language Toolkit (NLTK) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It also features neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT.
CoreNLP is a set of natural language analysis tools written in Java. CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.
NLPnet is a Python library for Natural Language Processing tasks based on neural networks. It performs part-of-speech tagging, semantic role labeling and dependency parsing.
Flair is a simple framework for state-of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.
Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Apache OpenNLP is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like Named Entity Recognition, Sentence Detection, POS(Part-Of-Speech) tagging, Tokenization Feature extraction, Chunking, Parsing, and Coreference resolution.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
Volver a la cima
Bioinformatics is a field of computational science that has to do with the analysis of sequences of biological molecules. This usually refers to genes, DNA, RNA, or protein, and is particularly useful in comparing genes and other sequences in proteins and other sequences within an organism or between organisms, looking at evolutionary relationships between organisms, and using the patterns that exist across DNA and protein sequences to figure out what their function is.
European Bioinformatics Institute
Centro Nacional de Información Biotecnológica
Online Courses in Bioinformatics |ISCB - International Society for Computational Biology
Bioinformatics | Coursera
Top Bioinformatics Courses | Udemy
Biometrics Courses | Udemy
Learn Bioinformatics with Online Courses and Lessons | edX
Bioinformatics Graduate Certificate | Harvard Extension School
Bioinformatics and Biostatistics | UC San Diego Extension
Bioinformatics and Proteomics - Free Online Course Materials | MIT
Introduction to Biometrics course - Biometrics Institute
Bioconductor is an open source project that provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an AMI (Amazon Machine Image) and Docker images.
Bioconda is a channel for the conda package manager specializing in bioinformatics software. It has a repository of packages containing over 7000 bioinformatics packages ready to use with conda install.
UniProt is a freely accessible database that provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information.
Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.
Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics.
BioRuby is a toolkit that has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO.
BioJava is a toolkit that provides an API to maintain local installations of the PDB, load and manipulate structures, perform standard analysis such as sequence and structure alignments and visualize them in 3D.
BioPHP is an open source project that provides a collection of open source PHP code, with classes for DNA and protein sequence analysis, alignment, database parsing, and other bioinformatics tools.
Avogadro is an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.
Ascalaph Designer is a program for molecular dynamic simulations. Under a single graphical environment are represented as their own implementation of molecular dynamics as well as the methods of classical and quantum mechanics of popular programs.
Anduril is a workflow platform for analyzing large data sets. Anduril provides facilities for analyzing high-thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.
Galaxy is an open source, web-based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.
PathVisio is a free open-source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.
Orange is a powerful data mining and machine learning toolkit that performs data analysis and visualization.
Basic Local Alignment Search Tool is a tool that finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.
OSIRIS is public-domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.
NCBI BioSystems is a Database that provides integrated access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez.
Volver a la cima
CUDA Toolkit. Source: NVIDIA Developer CUDA
CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
CUDA Toolkit Documentation
CUDA Quick Start Guide
CUDA on WSL
CUDA GPU support for TensorFlow
NVIDIA Deep Learning cuDNN Documentation
NVIDIA GPU Cloud Documentation
NVIDIA NGC is a hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
NVIDIA NGC Containers is a registry that provides researchers, data scientists, and developers with simple access to a comprehensive catalog of GPU-accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
CUDA Toolkit is a collection of tools & libraries that provide a development environment for creating high performance GPU-accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
CUDA-X HPC is a collection of libraries, tools, compilers and APIs that help developers solve the world's most challenging problems. CUDA-X HPC includes highly tuned kernels essential for high-performance computing (HPC).
NVIDIA Container Toolkit is a collection of tools & libraries that allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime library and utilities to automatically configure containers to leverage NVIDIA GPUs.
Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
CUB is a cooperative primitives for CUDA C++ kernel authors.
Tensorman is a utility for easy management of Tensorflow containers by developed by System76.Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.
CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
ArrayFire is a general-purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
Thrust is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
AresDB is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
Arraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
Kintinuous is a real-time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
GraphVite is a general graph embedding engine, dedicated to high-speed and large-scale embedding learning in various applications.
Volver a la cima
MATLAB is a programming language that does numerical computing such as expressing matrix and array mathematics directly.
MATLAB Documentation
Getting Started with MATLAB
MATLAB and Simulink Training from MATLAB Academy
MathWorks Certification Program
MATLAB Online Courses from Udemy
MATLAB Online Courses from Coursera
MATLAB Online Courses from edX
Building a MATLAB GUI
MATLAB Style Guidelines 2.0
Setting Up Git Source Control with MATLAB & Simulink
Pull, Push and Fetch Files with Git with MATLAB & Simulink
Create New Repository with MATLAB & Simulink
PRMLT is Matlab code for machine learning algorithms in the PRML book.
MATLAB and Simulink Services & Applications List
MATLAB in the Cloud is a service that allows you to run in cloud environments from MathWorks Cloud to Public Clouds including AWS and Azure.
MATLAB Online™ is a service that allows to users to uilitize MATLAB and Simulink through a web browser such as Google Chrome.
Simulink is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
Simulink Online™ is a service that provides access to Simulink through your web browser.
MATLAB Drive™ is a service that gives you the ability to store, access, and work with your files from anywhere.
MATLAB Parallel Server™ is a tool that lets you scale MATLAB® programs and Simulink® simulations to clusters and clouds. You can prototype your programs and simulations on the desktop and then run them on clusters and clouds without recoding. MATLAB Parallel Server supports batch jobs, interactive parallel computations, and distributed computations with large matrices.
MATLAB Schemer is a MATLAB package makes it easy to change the color scheme (theme) of the MATLAB display and GUI.
LRSLibrary is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Statistics and Machine Learning Toolbox™ is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
Partial Differential Equation Toolbox™ is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
SoC Blockset™ is a tool that provides Simulink® blocks and visualization tools for modeling, simulating, and analyzing hardware and software architectures for ASICs, FPGAs, and systems on a chip (SoC). You can build your system architecture using memory models, bus models, and I/O models, and simulate the architecture together with the algorithms.
Wireless HDL Toolbox™ is a tool that provides pre-verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
ThingSpeak™ is an IoT analytics service that allows you to aggregate, visualize, and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak, you can perform online analysis and process data as it comes in. ThingSpeak is often used for prototyping and proof-of-concept IoT systems that require analytics.
SEA-MAT is a collaborative effort to organize and distribute Matlab tools for the Oceanographic Community.
Gramm is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
hctsa is a software package for running highly comparative time-series analysis using Matlab.
Plotly is a Graphing Library for MATLAB.
YALMIP is a MATLAB toolbox for optimization modeling.
GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
Volver a la cima
C++ is a cross-platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
C is a general-purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
Embedded C is a set of language extensions for the C programming language by the C Standards Committee to address issues that exist between C extensions for different embedded systems. The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
C & C++ Developer Tools from JetBrains
Open source C++ libraries on cppreference.com
C++ Graphics libraries
C++ Libraries in MATLAB
C++ Tools and Libraries Articles
Google C++ Style Guide
Introduction C++ Education course on Google Developers
C++ style guide for Fuchsia
C and C++ Coding Style Guide by OpenTitan
Chromium C++ Style Guide
C++ Core Guidelines
C++ Style Guide for ROS
Learn C++
Learn C : An Interactive C Tutorial
C++ Institute
C++ Online Training Courses on LinkedIn Learning
C++ Tutorials on W3Schools
Learn C Programming Online Courses on edX
Learn C++ with Online Courses on edX
Learn C++ on Codecademy
Coding for Everyone: C and C++ course on Coursera
C++ For C Programmers on Coursera
Top C Courses on Coursera
C++ Online Courses on Udemy
Top C Courses on Udemy
Basics of Embedded C Programming for Beginners on Udemy
C++ For Programmers Course on Udacity
C++ Fundamentals Course on Pluralsight
Introduction to C++ on MIT Free Online Course Materials
Introduction to C++ for Programmers | harvard
Online C Courses | Universidad de Harvard
AWS SDK for C++
Azure SDK for C++
Azure SDK for C
C++ Client Libraries for Google Cloud Services
Visual Studio is an integrated development environment (IDE) from Microsoft; which is a feature-rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
Visual Studio Code es un editor de código redefinido y optimizado para crear y depurar aplicaciones web y en la nube modernas.
Vcpkg is a C++ Library Manager for Windows, Linux, and MacOS.
ReSharper C++ is a Visual Studio Extension for C++ developers developed by JetBrains.
AppCode is constantly monitoring the quality of your code. It warns you of errors and smells and suggests quick-fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
CLion is a cross-platform IDE for C and C++ developers developed by JetBrains.
Code::Blocks is a free C/C++ and Fortran IDE built to meet the most demanding needs of its users. It is designed to be very extensible and fully configurable. Built around a plugin framework, Code::Blocks can be extended with plugins.
CppSharp is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem. It consumes C/C++ header and library files and generates the necessary glue code to surface the native API as a managed API. Such an API can be used to consume an existing native library in your managed code or add managed scripting support to a native codebase.
Conan is an Open Source Package Manager for C++ development and dependency management into the 21st century and on par with the other development ecosystems.
High Performance Computing (HPC) SDK is a comprehensive toolbox for GPU accelerating HPC modeling and simulation applications. It includes the C, C++, and Fortran compilers, libraries, and analysis tools necessary for developing HPC applications on the NVIDIA platform.
Thrust is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
Boost is an educational opportunity focused on cutting-edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
Automake is a tool for automatically generating Makefile.in files compliant with the GNU Coding Standards. Automake requires the use of GNU Autoconf.
Cmake is an open-source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
GDB is a debugger, that allows you to see what is going on `inside' another program while it executes or what another program was doing at the moment it crashed.
GCC is a compiler Collection that includes front ends for C, C++, Objective-C, Fortran, Ada, Go, and D, as well as libraries for these languages.
GSL is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite.
OpenGL Extension Wrangler Library (GLEW) is a cross-platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
Libtool is a generic library support script that hides the complexity of using shared libraries behind a consistent, portable interface. To use Libtool, add the new generic library building commands to your Makefile, Makefile.in, or Makefile.am.
Maven is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.
TAU (Tuning And Analysis Utilities) is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements as well as event-based sampling. All C++ language features are supported including templates and namespaces.
Clang is a production quality C, Objective-C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
OpenCV is a highly optimized library with focus on real-time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Libcu++ is the NVIDIA C++ Standard Library for your entire system. It provides a heterogeneous implementation of the C++ Standard Library that can be used in and between CPU and GPU code.
ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface that makes it easy to respond to the recognition of phrases of interest.
Oat++ is a light and powerful C++ web framework for highly scalable and resource-efficient web application. It's zero-dependency and easy-portable.
JavaCPP is a program that provides efficient access to native C++ inside Java, not unlike the way some C/C++ compilers interact with assembly language.
Cython is a language that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations such as calling C functions and declaring C types on variables and class attributes.
Spdlog is a very fast, header-only/compiled, C++ logging library.
Infer is a static analysis tool for Java, C++, Objective-C, and C. Infer is written in OCaml.
Volver a la cima
Java is a popular programming language and development platform(JDK). It reduces costs, shortens development timeframes, drives innovation, and improves application services. With millions of developers running more than 51 billion Java Virtual Machines worldwide.
The Eclipse Foundation is home to a worldwide community of developers, the Eclipse IDE, Jakarta EE and over 375 open source projects, including runtimes, tools and frameworks for Java and other languages.
Getting Started with Java
Oracle Java certifications from Oracle University
Google Developers Training
Google Developers Certification
Java Tutorial by W3Schools
Building Your First Android App in Java
Getting Started with Java in Visual Studio Code
Google Java Style Guide
AOSP Java Code Style for Contributors
Chromium Java style guide
Get Started with OR-Tools for Java
Getting started with Java Tool Installer task for Azure Pipelines
Gradle User Manual
Java SE contains several tools to assist in program development and debugging, and in the monitoring and troubleshooting of production applications.
JDK Development Tools includes the Java Web Start Tools (javaws) Java Troubleshooting, Profiling, Monitoring and Management Tools (jcmd, jconsole, jmc, jvisualvm); and Java Web Services Tools (schemagen, wsgen, wsimport, xjc).
Android Studio is the official integrated development environment for Google's Android operating system, built on JetBrains' IntelliJ IDEA software and designed specifically for Android development. Availble on Windows, macOS, Linux, Chrome OS.
IntelliJ IDEA is an IDE for Java, but it also understands and provides intelligent coding assistance for a large variety of other languages such as Kotlin, SQL, JPQL, HTML, JavaScript, etc., even if the language expression is injected into a String literal in your Java code.
NetBeans is an IDE provides Java developers with all the tools needed to create professional desktop, mobile and enterprise applications. Creating, Editing, and Refactoring. The IDE provides wizards and templates to let you create Java EE, Java SE, and Java ME applications.
Java Design Patterns is a collection of the best formalized practices a programmer can use to solve common problems when designing an application or system.
Elasticsearch is a distributed RESTful search engine built for the cloud written in Java.
RxJava is a Java VM implementation of Reactive Extensions: a library for composing asynchronous and event-based programs by using observable sequences. It extends the observer pattern to support sequences of data/events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.
Guava is a set of core Java libraries from Google that includes new collection types (such as multimap and multiset), immutable collections, a graph library, and utilities for concurrency, I/O, hashing, caching, primitives, strings, and more! It is widely used on most Java projects within Google, and widely used by many other companies as well.
okhttp is a HTTP client for Java and Kotlin developed by Square.
Retrofit is a type-safe HTTP client for Android and Java develped by Square.
LeakCanary is a memory leak detection library for Android develped by Square.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities with elegant and fluent APIs in Java and Scala.
Fastjson is a Java library that can be used to convert Java Objects into their JSON representation. It can also be used to convert a JSON string to an equivalent Java object.
libGDX is a cross-platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
Jenkins is the leading open-source automation server. Built with Java, it provides over 1700 plugins to support automating virtually anything, so that humans can actually spend their time doing things machines cannot.
DBeaver is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports any database which has JDBC driver (which basically means - ANY database). EE version also supports non-JDBC datasources (MongoDB, Cassandra, Redis, DynamoDB, etc).
Redisson is a Redis Java client with features of In-Memory Data Grid. Over 50 Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Tomcat, Scheduler, JCache API, Hibernate, MyBatis, RPC, and local cache.
GraalVM is a universal virtual machine for running applications written in JavaScript, Python, Ruby, R, JVM-based languages like Java, Scala, Clojure, Kotlin, and LLVM-based languages such as C and C++.
Gradle is a build automation tool for multi-language software development. From mobile apps to microservices, from small startups to big enterprises, Gradle helps teams build, automate and deliver better software, faster. Write in Java, C++, Python or your language of choice.
Apache Groovy is a powerful, optionally typed and dynamic language, with static-typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming.
JaCoCo is a free code coverage library for Java, which has been created by the EclEmma team based on the lessons learned from using and integration existing libraries for many years.
Apache JMeter is used to test performance both on static and dynamic resources, Web dynamic applications. It also used to simulate a heavy load on a server, group of servers, network or object to test its strength or to analyze overall performance under different load types.
Junit is a simple framework to write repeatable tests. It is an instance of the xUnit architecture for unit testing frameworks.
Mockito is the most popular Mocking framework for unit tests written in Java.
SpotBugs is a program which uses static analysis to look for bugs in Java code.
SpringBoot is a great tool that helps you to create Spring-powered, production-grade applications and services with absolute minimum fuss. It takes an opinionated view of the Spring platform so that new and existing users can quickly get to the bits they need.
YourKit is a technology leader, creator of the most innovative and intelligent tools for profiling Java & .NET applications.
Volver a la cima
Python is an interpreted, high-level programming language. Python is used heavily in the fields of Data Science and Machine Learning.
Python Developer's Guide is a comprehensive resource for contributing to Python – for both new and experienced contributors. It is maintained by the same community that maintains Python.
Azure Functions Python developer guide is an introduction to developing Azure Functions using Python. The content below assumes that you've already read the Azure Functions developers guide.
CheckiO is a programming learning platform and a gamified website that teaches Python through solving code challenges and competing for the most elegant and creative solutions.
Python Institute
PCEP – Certified Entry-Level Python Programmer certification
PCAP – Certified Associate in Python Programming certification
PCPP – Certified Professional in Python Programming 1 certification
PCPP – Certified Professional in Python Programming 2
MTA: Introduction to Programming Using Python Certification
Getting Started with Python in Visual Studio Code
Google's Python Style Guide
Google's Python Education Class
Real Python
The Python Open Source Computer Science Degree by Forrest Knight
Intro to Python for Data Science
Intro to Python by W3schools
Codecademy's Python 3 course
Learn Python with Online Courses and Classes from edX
Python Courses Online from Coursera
Python Package Index (PyPI) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community.
PyCharm is the best IDE I've ever used. With PyCharm, you can access the command line, connect to a database, create a virtual environment, and manage your version control system all in one place, saving time by avoiding constantly switching between windows.
Python Tools for Visual Studio(PTVS) is a free, open source plugin that turns Visual Studio into a Python IDE. It supports editing, browsing, IntelliSense, mixed Python/C++ debugging, remote Linux/MacOS debugging, profiling, IPython, and web development with Django and other frameworks.
Pylance is an extension that works alongside Python in Visual Studio Code to provide performant language support. Under the hood, Pylance is powered by Pyright, Microsoft's static type checking tool.
Pyright is a fast type checker meant for large Python source bases. It can run in a “watch” mode and performs fast incremental updates when files are modified.
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.
Web2py is an open-source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.
AWS Chalice is a framework for writing serverless apps in python. It allows you to quickly create and deploy applications that use AWS Lambda.
Tornado is a Python web framework and asynchronous networking library. Tornado uses a non-blocking network I/O, which can scale to tens of thousands of open connections.
HTTPie is a command line HTTP client that makes CLI interaction with web services as easy as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.
Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
Sentry is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application.
Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library.
CherryPy is a minimalist Python object-oriented HTTP web framework.
Sanic is a Python 3.6+ web server and web framework that's written to go fast.
Pyramid is a small and fast open source Python web framework. It makes real-world web application development and deployment more fun and more productive.
TurboGears is a hybrid web framework able to act both as a Full Stack framework or as a Microframework.
Falcon is a reliable, high-performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
Neural Network Intelligence(NNI) is an open source AutoML toolkit for automate machine learning lifecycle, including Feature Engineering, Neural Architecture Search, Model Compression and Hyperparameter Tuning.
Dash is a popular Python framework for building ML & data science web apps for Python, R, Julia, and Jupyter.
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built-in.
Locust is an easy to use, scriptable and scalable performance testing tool.
spaCy is a library for advanced Natural Language Processing in Python and Cython.
NumPy is the fundamental package needed for scientific computing with Python.
Pillow is a friendly PIL(Python Imaging Library) fork.
IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.
GraphLab Create is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance machine learning models.
Pandas is a fast, powerful, and easy to use open source data structrures, data analysis and manipulation tool, built on top of the Python programming language.
PuLP is an Linear Programming modeler written in python. PuLP can generate LP files and call on use highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI, to solve these linear problems.
Matplotlib is a 2D plotting library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
Volver a la cima
Scala is a combination of object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.
Scala Style Guide
Databricks Scala Style Guide
Data Science using Scala and Spark on Azure
Creating a Scala Maven application for Apache Spark in HDInsight using IntelliJ
Intro to Spark DataFrames using Scala with Azure Databricks
Using Scala to Program AWS Glue ETL Scripts
Using Flink Scala shell with Amazon EMR clusters
AWS EMR and Spark 2 using Scala from Udemy
Using the Google Cloud Storage connector with Apache Spark
Write and run Spark Scala jobs on Cloud Dataproc for Google Cloud
Scala Courses and Certifications from edX
Scala Courses from Coursera
Top Scala Courses from Udemy
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Play Framework is a web framework combines productivity and performance making it easy to build scalable web applications with Java and Scala.
Dotty is a research compiler that will become Scala 3.
AWScala is a tool that enables Scala developers to easily work with Amazon Web Services in the Scala way.
Scala.js is a compiler that converts Scala to JavaScript.
Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.
Scala Native is an optimizing ahead-of-time compiler and lightweight managed runtime designed specifically for Scala.
Gitbucket is a Git platform powered by Scala with easy installation, high extensibility & GitHub API compatibility.
Finagle is a fault tolerant, protocol-agnostic RPC system
Gatling is a load test tool. It officially supports HTTP, WebSocket, Server-Sent-Events and JMS.
Scalatra is a tiny Scala high-performance, async web framework, inspired by Sinatra.
Volver a la cima
R is an open source software environment for statistical computing and graphics. It compiles and runs on a wide variety of platforms such as Windows and MacOS.
An Introduction to R
Google's R Style Guide
R developer's guide to Azure
Running R at Scale on Google Compute Engine
Running R on AWS
RStudio Server Pro for AWS
Learn R by Codecademy
Learn R Programming with Online Courses and Lessons by edX
R Language Courses by Coursera
Learn R For Data Science by Udacity
RStudio is an integrated development environment for R and Python, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
Shiny is a newer package from RStudio that makes it incredibly easy to build interactive web applications with R.
Rmarkdown is a package helps you create dynamic analysis documents that combine code, rendered output (such as figures), and prose.
Rplugin is R Language supported plugin for the IntelliJ IDE.
Plotly is an R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js.
Metaflow is a Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.
LightGBM is a gradient boosting framework that uses tree based learning algorithms, used for ranking, classification and many other machine learning tasks.
Dash is a Python framework for building analytical web applications in Python, R, Julia, and Jupyter.
MLR is Machine Learning in R.
ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Plumber is a tool that allows you to create a web API by merely decorating your existing R source code with special comments.
Drake is an R-focused pipeline toolkit for reproducibility and high-performance computing.
DiagrammeR is a package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into R Markdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files.
Knitr is a general-purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
Broom is a tool that converts statistical analysis objects from R into tidy format.
Volver a la cima
Julia is a high-level, high-performance dynamic language for technical computing. Julia programs compile to efficient native code for multiple platforms via LLVM.
JuliaHub contains over 4,000 Julia packages for use by the community.
Julia Observer
Julia Manual
JuliaLang Essentials
Julia Style Guide
Julia By Example
JuliaLang Gitter
DataFrames Tutorial using Jupyter Notebooks
Julia Academy
Julia Meetup groups
Julia on Microsoft Azure
JuliaPro is a free and fast way to setup Julia for individual researchers, engineers, scientists, quants, traders, economists, students and others. Julia developers can build better software quicker and easier while benefiting from Julia's unparalleled high performance. It includes 2600+ open source packages or from a curated list of 250+ JuliaPro packages. Curated packages are tested, documented and supported by Julia Computing.
Juno is a powerful, free IDE based on Atom for the Julia language.
Debugger.jl is the Julia debuggin tool.
Profile (Stdlib) is a module provides tools to help developers improve the performance of their code. When used, it takes measurements on running code, and produces output that helps you understand how much time is spent on individual line's.
Revise.jl allows you to modify code and use the changes without restarting Julia. With Revise, you can be in the middle of a session and then update packages, switch git branches, and/or edit the source code in the editor of your choice; any changes will typically be incorporated into the very next command you issue from the REPL. This can save you the overhead of restarting Julia, loading packages, and waiting for code to JIT-compile.
JuliaGPU is a Github organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
IJulia.jl is the Julia kernel for Jupyter.
AWS.jl is a Julia interface for Amazon Web Services.
CUDA.jl is a package for the main programming interface for working with NVIDIA CUDA GPUs using Julia. It features a user-friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
XLA.jl is a package for compiling Julia to XLA for Tensor Processing Unit(TPU).
Nanosoldier.jl is a package for running JuliaCI services on MIT's Nanosoldier cluster.
Julia for VSCode is a powerful extension for the Julia language.
JuMP.jl is a domain-specific modeling language for mathematical optimization embedded in Julia.
Optim.jl is a univariate and multivariate optimization in Julia.
RCall.jl is a package that allows you to call R functions from Julia.
JavaCall.jl is a package that allows you to call Java functions from Julia.
PyCall.jl is a package that allows you to call Python functions from Julia.
MXNet.jl is the Apache MXNet Julia package. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia.
Knet is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia.
Distributions.jl is a Julia package for probability distributions and associated functions.
DataFrames.jl is a tool for working with tabular data in Julia.
Flux.jl is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.
IRTools.jl is a simple and flexible IR format, expressive enough to work with both lowered and typed Julia code, as well as external IRs.
Cassette.jl is a Julia package that provides a mechanism for dynamically injecting code transformation passes into Julia's just-in-time (JIT) compilation cycle, enabling post hoc analysis and modification of "Cassette-unaware" Julia programs without requiring manual source annotation or refactoring of the target code.
Volver a la cima
Distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) Public License.
